1. Deep learning, artificial intelligence, and bioinformatics promises innovations and imminent forecasts in SARS-COVID-19 genome data analysisS. Sheik Asraf, P. Nagaraj, and V. Muneeswaran1.1 Introduction1.2 COVID-19-a global pandemic1.3 Genomics of COVID-191.4 Applications of deep learning in COVID-19 genomics studies1.5 Role of artificial intelligence in COVID-19 genomics research1.6 Usage of bioinformatics tools, software, and databases in COVID-19 genomics investigation1.7 Challenges and prospects of deep learning, artificial intelligence, and bioinformatics in COVID-19 genomics1.8 ConclusionReferences2. Integration of IoT and AI for potato leaf disease detection: enhancing agricultural efficiency and sustainabilityE. Senthamil Selvi and S. Anusuya2.1 Introduction2.2 Literature survey2.3 Classification process for potato leaf diseases2.4 Image preliminary processing2.5 Image augmentation2.6 Feature extraction2.7 Evaluation and recognition2.8 Methods and materials2.9 Transfer learning2.10 Pretrained network model2.11 Proposed model2.12 Result and discussion2.13 Conclusion2.14 Future workReferences3. A hybridized long-short-term memory networks-based deep learning model using reptile search optimization for COVID-19 predictionBalakrishnama Manohar, Raja Das, Potharla Ramadevi, and Balamurugan Balusamy3.1 Introduction3.2 Materials and methods3.3 Data preprocessing3.4 Data normalization3.5 Proposed methodology3.6 Methodology3.7 Reptile search algorithm3.8 Encircling phase (global search or exploration)3.9 Hunting phase (local search or exploitation)3.10 Optimized long-short-term memory networks-reptile search algorithm model3.11 Model evaluation3.12 Results3.13 ConclusionReferences4. Improving coronavirus classification accuracy with transfer learning and chest radiograph analysisM. Lakshmi, Raja Das, Balakrishnama Manohar, and Balamurugan Balusamy4.1 Introduction4.2 Related works4.3 Materials and methods4.4 Results and discussion4.5 ConclusionReferences5. A hybrid deep neural network using the Levenberg-Marquardt algorithm applied to the nonlinear magnetohydrodynamic Jeffery-Hamel blood flow problemPriyanka Chandra, Raja Das, and Smita Sharma5.1 Introduction5.2 Mathematical modeling5.3 Solution methodology5.4 Result and discussion5.5 ConclusionEthical statementAcknowledgmentDeclaration of interest statementFundingData availability statementReferences6. An image segmentation method using intuitionistic fuzzy k-means and convolutional neural networks in multiclass image classificationPotharla Ramadevi, Raja Das, M. Lakshmi, Balakrishnama Manohar, and Smita Sharma6.1 Introduction6.2 Related works6.3 Methodology6.4 Results and discussion6.5 ConclusionReferences7. Deep learning for wearable sensor data analysisP. Aakash Kumar, Abha Rani, S. Amutha, and B. Surendiran7.1 Introduction7.2 Literature review7.3 Methodology7.4 Result and discussion7.5 ConclusionReferences8. Unveiling emotions in real-time: a novel approach to face emotion recognitionGowthami V. and Vijayalakshmi R.8.1 Introduction8.2 Convolutional neural network8.3 Objective8.4 Literature survey8.5 Proposed work8.6 Pseudocode for training the model8.7 Results8.8 Future workReferencesFurther reading9. Unleashing the power of convolutional neural networks for diabetic retinopathy detection in ophthalmologyGowthami V. and K. Alamelu9.1 Introduction9.2 Literature review9.3 System methodology9.4 Result and discussion9.5 Conclusion and future workReferences10. Case studies and use cases of deep learning for biomedical applicationsAmutha Prabakar Muniyandi, Padmavathy T., and Balamurugan Balusamy10.1 Introduction10.2 Impact of deep learning in bio-engineering10.3 Evolution of artificial neural networks10.4 Applications of deep learning-bioinformatics10.5 Explainable artificial intelligence in bioinformatics10.6 ConclusionReferences11. A convolutional neural network-based deep ensemble method for computed tomography scan image-based lung cancer diagnosisR.
